Adaptive digital filtering is used for self-interference reduction in simultaneous transmit and receiver (“STAR”) systems. Conventional adaptive filter techniques make use of correlations between the received waveform and the known transmitted waveform to automatically synthesize a filter structure which matches the physical response of the system. In this way, the self-interference can be accurately modeled and removed.
Channel noise and nonlinear distortion cannot be corrected by linear adaptive filters. This limits the effectiveness of digital cancellation. If an external signal of interest (“SOP”) is correlated with the transmitted waveform, then it will also be attenuated or removed by the adaptive canceller, thus rendering this technique ineffective for use in many practical systems, e.g. radars, comm. repeaters, digital radio frequency memories, etc.
Furthermore, the performance of computationally efficient filter algorithms (e.g. least mean square algorithms) depends on the characteristics of the transmitted waveform. Thus, systems that utilize many practical waveforms (e.g. narrowband comm., chirps) will converge extremely slowly. Also, multichannel cancellation (e.g. for STAR phased arrays) will have very poor convergence due to high correlation between transmit channels.
For multichannel systems, existing solutions are computationally expensive and require central processing of all data to compute cross-correlations. This scales poorly as the number of channels increases.